Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Using ChatGPT as a “Friend” in reflective critical friendships
1
Zitationen
2
Autoren
2025
Jahr
Abstract
Abstract Reflective practice has long been regarded as a cornerstone of language teacher education and development, typically enacted through dialogue with trusted colleagues or critical friendships (CFs). Such human-to-human exchanges are grounded in trust, contextual understanding, and professional experience, and they play a central role in shaping teachers’ professional development. The emergence of generative artificial intelligence (AI) tools such as ChatGPT, however, invites new possibilities for reflection by offering teachers instant access to dialogue that is probing, structured, and nonjudgmental. This study examines the potential of ChatGPT to act as a form of CF in reflective practice, comparing its contributions with those of human interlocutors. We consider benefits such as immediacy, breadth of perspectives, and creative provocation, alongside significant limitations, including the absence of trust, lived experience, and relational aspects. Ethical concerns, particularly those relating to bias, cultural representation, privacy, and the risk of over-reliance, are also foregrounded. We argue that while ChatGPT may usefully supplement reflective practice, especially for teachers with limited access to professional networks, it cannot replace the dialogic, situated, and relational qualities of human CF. We conclude by proposing that language teacher education should integrate critical AI literacy, ethical guidelines, and approaches that combine AI-mediated dialogue with human interlocutors. In doing so, reflective practice can be enriched rather than diminished by the limitations of ChatGPT.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.611 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.504 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.025 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.835 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.